in silico Plants

While nitrogen fertilizers are widely used in agricultural production, their application incurs significant environmental and energetic costs. In contrast, some crops are less dependent on these fertilizers because they engage in symbioses with rhizobia, nitrogen-fixing bacteria provide ammonium to the plant in exchange for carbon. However, the carbon cost associated with nitrogen fixation can negatively impact crop yields. Improving the efficiency of this metabolic process could alleviate this impact on crop productivity. Mathematical models can help us quantitatively explore metabolic behavior and identify potential targets for metabolic engineering. In this work, we developed a kinetic model of determinate root nodule metabolism, where this symbiotic exchange of carbon from the plant and nitrogen from the bacteria occurs. We used this model to evaluate how the predicted metabolic behavior differs between inefficient and efficient nodules, and to identify potential engineering targets for improving nitrogen fixation efficiency and rate. We show that the enzymes phosphoenolpyruvate carboxylase and pyruvate kinase have significant influence on the predicted rate and efficiency of nitrogen fixation, especially when their expression is varied in combination with oxidative Pentose Phosphate Pathway enzymes like glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase. The model predicts that pairing a 3-fold decrease in glucose-6-phosphate dehydrogenase activity along with either a 3-fold increase in phosphoenolpyruvate carboxylase activity or decrease in pyruvate kinase activity could increase nitrogen fixation rate by 5.51% while improving nitrogen fixation efficiency by 7.74%.

Evolving agricultural practices and contexts invite to reconsider the way crop and plant models represent agroecosystem processes. Crop models assume spatial homogeneity, which reduces confidence in their predictions for structurally heterogeneous systems, whereas FSPMs complexity limits their application to field-scale or large-scale studies. To benefit from strengths of both approaches, we introduce ArchiCrop, a parametric 3D architectural model of cereals that generates plant geometries constrained by crop model dynamics and coordination rules. Inspired by the concept of equifinality, ArchiCrop generates a morphospace of architecturally diverse morphotypes which remain equivalent at crop scale in terms of LAI and height. This multiscale approach enables the comparison of processes computed at different scales on wheat, rice, maize and sorghum. We demonstrate its application evaluating light interception Beers formalism in STICS soil-crop model relying on the leaf-resolved radiosity model Caribu for the 3D reference simulations, for a sorghum monocrop. We show that the consideration of the variability of only two plant architectural traits, leaf insertion angle and leaf number, introduces up to 27% of uncertainty in the cumulated absorbed light at the end of the season. A possible outcome from this method is also the definition of metamodels for crop model processes, as exemplified for extinction coefficient of Beers law. ArchiCrop can support a range of applications, including crop model uncertainty analysis, model-assisted phenotyping, and ideotype design. HighlightsO_LIArchiCrop is the first 3D+t botany-based parametric generative model for cereals. C_LIO_LIArchiCrop downscales crop model dynamics to 3D+t architecture canopies efficiently. C_LIO_LIArchiCrop compares big leaf versus leaf-resolved light interception. C_LIO_LIPlant architectures with same leaf area intercept light with up to 27% variability. C_LIO_LIArchiCrop helps ideotyping, crop model evaluation and error propagation analysis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/716970v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@6534b3org.highwire.dtl.DTLVardef@66df41org.highwire.dtl.DTLVardef@1cb4c9dorg.highwire.dtl.DTLVardef@12fa30_HPS_FORMAT_FIGEXP M_FIG C_FIG

O_LIBackground and Aims Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. C_LIO_LIMethods We used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data. C_LIO_LIKey Results The method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters. C_LIO_LIConclusions The method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models. C_LI

Computational modelling has become essential to advancing our understanding of plant developmental and physiological processes, necessitating the development of new computational approaches and software. Here, we present VirtualLeaf-2.0, an updated version of this modelling framework for the biophysical and biomechanical interactions between cells in plant tissues, with novel features for more detailed modelling of the cell wall. In particular, the updated version of VirtualLeaf enables detailed modelling of variations in cell wall stability and cell wall sliding up to the level of individual cell wall elements. The plant cell wall plays a pivotal role in plant development and survival, with younger cells generally having thinner, more flexible (primary) walls than older cells. Cell wall stability is further affected by signalling in growth processes and pathogen infection. The improvements of VirtualLeaf lay the groundwork for using VirtualLeaf to address novel questions involving plant tissue dynamics during growth, tissue formation and pathogen defence, as illustrated with example simulations.

Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. HighlightsO_LIIntegrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited. C_LI

Plants use light energy to produce ATP and redox equivalents for metabolism. Since during the course of a day plants are exposed to constantly fluctuating light, the supply of ATP and redox equivalents is also fluctuating. Further, if the metabolism cannot use all of the supplied energy, the excess absorbed energy can damage the plant in the form of reactive oxygen species. It is thus reasonable to assume that the metabolism downstream of the energy supply is dynamic and as being capable of dampening sudden spikes in supply is advantageous, it is further reasonable to assume that the immediate downstream metabolism is flexible as well. A flexible metabolism exposed to a fluctuating input is unlikely to be in metabolic steady-state, yet a lot of mathematical models for carbon fixation assume one for the Calvin-Benson-Bassham (CBB) cycle. Here we present an analysis of the validity of this assumption by progressively simplifying an existing model of photosynthesis and carbon fixation.

With a changing climate, disease pressure, and other production threats, it is critical to ensure that crop producers are well-positioned to protect and optimize yields. In this work we present LYM-1, the first large-scale, multi-crop model for the prediction of yield performance in the Canadian prairies. This is enabled by a large dataset containing over 4.7 million yield observations across 10 different crop types, distributed over 23 growing years. Leveraging additional data sources for weather and soil properties allows the model to reason about the complex interactions between genetics, environment, and management which underlie yield. The trained model is not only effective at predicting the yield for held-out data, but also reveals scientifically and agronomically relevant effects such as the interaction between solar radiation and nitrogen uptake. We anticipate that large yield models can be used for both the optimization of crop production by producers, as well as by plant breeders and industry for crop design.

As metabolism is a very complex process, it is essential to use computational modeling to better understand the changes in metabolism at a systems level. In this paper, we used a computational model that simulates the metabolism of mature plants to test how the use of nitrate and ammonium as nitrogen sources affects plant metabolism. Our model predicted and showed the possible changes in metabolism in different plant tissues in response to the use of different nitrogen sources. Our methodology produces predictions of metabolic fluxes in plants and improves our understanding of how plants react to changes in conditions. This research can potentially give insights into how we can improve crop yield by identifying metabolic processes that are important in plant growth and in the adaptation to different nitrogen sources. In future research work, we can apply the existing model to explore the effects of different nutrient availability under different conditions (e.g. well-watered vs drought) to optimize nutrient availability under particular environmental conditions. Experimental researchers, plant breeders, and farmers can use the knowledge gained from the modeling work to further our understanding of plant metabolism and apply the knowledge to improve plant growth in the lab and the field.

Crop models need to be regularly upgraded with parametrization for new cultivars but this requires calibration, which is a major challenge. With winter wheat cultivar Rubisko as a case study, we propose to apply a calibration protocol to estimate the parameters of this new cultivar with multi-trials experimental data. We tested the calibration protocol in different conditions including or not LAI and/or biomass experimental data and we found that the resulting LAI and biomass dynamics strongly diverge. Several key findings emerge from this study: (1) RUE parameters should be excluded from the calibration process, as their critical role in biomass dynamics causes the optimization algorithm to treat them as adjustment parameters, resulting in unrealistic values for multiple parameters; (2) either LAI or biomass variables alone are sufficient for calibration, enabling experimental efforts to focus on one variable rather than both; and (3) the use of a synthetic dataset has facilitated the identification of the optimal type and timing of data collection needed to parameterize a new variety in the model. Moreover, the proposed methodology offers extrapolatable solutions applicable to other contexts (e.g., different models or datasets) and provides guidance on acquiring the most effective dataset for optimal calibration. The unbalanced structure of our dataset also highlighted the need to mobilize other calibration criteria (weighted RMSE) and alternative solutions to bridge the gap between quantitative metrics and empirical visual assessments.

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method "cut and flow" is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

AO_SCPLOWBSTRACTC_SCPLOWThe Farquhar-von-Caemmerer-Berry (FvCB) model is the most widely-used mechanistic model of C3 net CO2 assimilation, and it plays a significant role in plant physiology, ecology, climate science, and Earth system modeling. As use of the model has grown, multiple variants have appeared across publications. Although many of these are commonly used, there has not been a detailed investigation of existing variants and their impacts on results and interpretations. Here we summarize the types of variants and their prevalence in the literature, and we present a comprehensive comparison of differences between them. A key finding is that a common variant that uses the minimum of assimilation rates rather than the minimum of carboxylation rates, which we call the "min-A variant," makes different predictions than the original "min-W variant," yet appears in approximately half of highly-cited publications and software tools that use the FvCB model. Another concern is that although leaf biochemistry restricts the range of CO2 partial pressures where limitations due to triose phosphate utilization (TPU) can occur, this restriction is commonly omitted from the models equations. Among other potential issues, these variations can introduce errors exceeding 20% when estimating photosynthetic parameter values from CO2 response curves. It is therefore important to be aware of this source of error when fitting the model, to avoid using the min-A variant, and to include the biochemically-derived CO2 threshold for TPU limitations. CO_SCPLOWENTRALC_SCPLOW TO_SCPLOWHEMEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWOFC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWTHEC_SCPLOW MO_SCPLOWANUSCRIPTC_SCPLOWThe Farquhar-von-Caemmerer-Berry model of CO2 assimilation plays a key role in plant research, but many publications use variants of the model that differ from the original and can potentially introduce errors in photosynthetic parameter estimates. NO_SCPLOWOVELC_SCPLOW RO_SCPLOWESULTSC_SCPLOW, IO_SCPLOWDEASC_SCPLOWO_SCPCAP, C_SCPCAPO_SCPLOWORC_SCPLOW MO_SCPLOWETHODSC_SCPLOWUsing a literature survey, FvCB model variants are categorized, and some are found to make contradictory predictions. Comparisons against A-Ci curves show that the "min-W variant" exhibits the best performance, especially at low CO2 concentrations.

Reduced cortical cell files (CCFN) and enlarged cortical cells (CCS) reduce root maintenance costs. We used OpenSimRoot, a functional-structural model, to test the hypothesis that larger CCS, reduced CCFN, and their interactions with root cortical aerenchyma (RCA), are useful adaptations to suboptimal soil N, P, and K availability. Interactions of CCS and CCFN with lateral root branching density (LRBD) and increased carbon availability were evaluated under limited N, P and K availability. The combination of larger CCS and reduced CCFN increases the growth of maize up to 105%, 106%, and 144%, respectively, under limited N, P, or K availability. Interactions among larger CCS, reduced CCFN, and greater RCA results in combined growth benefits of up to 135%, 132%, and 161% under limited N, P, and K levels, respectively. Under low phosphorus and potassium availability, increased LRBD approximately doubles the utility of larger CCS and reduced CCFN. The utility of larger CCS and reduced CCFN is reduced by greater C availability as may occur in future climate scenarios. Our results support the hypothesis that larger CCS, reduced CCFN, and their interactions with RCA could increase nutrient acquisition by reducing root respiration and root nutrient demand. Phene synergisms may exist between CCS, CCFN, and LRBD. Natural genetic variation in CCS and CCFN merit consideration for breeding cereal crops with improved nutrient acquisition, which is critical for global food security.One sentence summary Functional-structural modeling indicates that enlarged root cortical cells and reduced cortical cell file number decrease root maintenance cost, permitting greater soil exploration, resource capture, and plant growth under suboptimal nitrogen, phosphorus and potassium availability.Abbreviations(CCS)Cortical cell size(CCFN)Cortical cell file number(RCA)Root cortical aerenchyma(SCD)Steep, Cheap and Deep(LRBD)Lateral root branching density(RHL)Root hair length(BRGA)Basal root growth angleView Full Text

Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Current phenotype prediction models do not adequately capture all of these fluctuations or effectively use genotype information. Instead, we have developed a dynamic modular approach that captures the genotype, environment, and Genotype-by-Environment effects to express the time-to-flowering phenotype in real time in Phaseolus vulgaris. The module we describe can be applied to different plant processes and can gradually replace processes in existing crop models. Our model can enable accelerated progress in diverse breeding programs, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can be achieved by implementing "smart" lighting regimens which in turn rely on a good understanding of how fluctuating light influences photosynthetic physiology. Here, a simple fit-for-purpose dynamic model is presented. It accurately predicts net leaf photosynthesis under natural fluctuating light. It comprises two ordinary differential equations predicting: 1) the total stomatal conductance to CO2 diffusion and 2) the CO2 concentration inside a leaf. It contains elements of the Farquhar-von Caemmerer-Berry model and the successful incorporation of this model suggests that for tomato (Solanum lycopersicum L.), it is sufficient to assume that Rubisco remains activated despite rapid fluctuations in irradiance. Furthermore, predictions of the net photosynthetic rate under both 400ppm and enriched 800ppm ambient CO2 concentrations indicate a strong correlation between the dynamic rate of photosynthesis and the rate of electron transport. Finally, we are able to indicate whether dynamic photosynthesis is Rubisco or electron transport rate limited. Author summaryThe cultivation of greenhouse crops under optimised conditions will become increasingly important, with supplemental lighting playing a vital role. However, converting light energy into plant photosynthesis is not always optimal. A potential venue that may lead to the efficient conversion of light energy involves a model-based implementation of "smart" lighting control strategy. This approach does however necessitate a good understanding of how plants harness light energy under natural fluctuating irradiance. Accordingly, as a first step, we have developed a small leaf-level model that predicts dynamic photosynthesis in natural fluctuating light. It may potentially be used in future supplemental light control applications.

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying effectively how the seasonal growth and yield dynamics of target crops might be affected over a wide range of environments is limited. Using a state-of-the-art cross-scale model we predicted crop-level impacts of a broad list of promising photosynthesis manipulation strategies for C3 wheat and C4 sorghum. The manipulation targets have varying effects on the enzyme-limited (Ac) and electron transport-limited (Aj) rates of photosynthesis. In the top decile of seasonal outcomes, yield gains with the list of manipulations were predicted to be modest, ranging between 0 and 8%, depending on the crop type and manipulation. To achieve the higher yield gains, large increases in both Ac and Aj are needed. This could likely be achieved by stacking Rubisco function and electron transport chain enhancements or installing a full CO2 concentrating system. However, photosynthetic enhancement influences the timing and severity of water and nitrogen stress on the crop, confounding yield outcomes. Strategies enhancing Ac alone offers more consistent but smaller yield gains across environments, Aj enhancement alone offers higher gains but is undesirable in less favourable environments. Understanding and quantifying complex cross-scale interactions between photosynthesis and crop yield will challenge and stimulate photosynthesis and crop research. Summary StatementLeaf-canopy-crop prediction using a state-of-the-art cross-scale model improves understanding of how photosynthetic manipulation alters wheat and sorghum growth and yield dynamics. This generates novel insights for quantifying impacts of photosynthetic enhancement on crop yield across environments.

Increasing winter temperatures jeopardize the yield of fruit trees requiring a prolonged and sufficiently cold winter to flower. Assessing the exact risk to different crop varieties is the first step in mitigating the harmful effect of climate change. Since empirically testing the impacts of many temperature scenarios is very time-consuming, quantitative predictive models could be extremely helpful in reducing the number of experiments needed. Here, we focus on olive (Olea europaea) - a traditional crop in the Mediterranean basin, a region expected to be severely affected by climatic change. Olive flowering and consequently yield depend on the sufficiency of cold periods and the lack of warm ones during the preceding winter. Yet, a satisfactory quantitative model forecasting its expected flowering under natural temperature conditions is still lacking. Previous models simply summed the number of cold hours during winter, as a proxy for flowering, but exhibited only mediocre agreement with empirical flowering values, possibly because they overlooked the order of occurrence of different temperatures. We empirically tested the effect of different temperature regimes on olive flowering intensity and flowering-gene expression. To predict flowering based on winter temperatures, we constructed a dynamic model, describing the response of a putative flowering factor to the temperature signal. The crucial ingredient in the model is an unstable intermediate, produced and degraded at temperature-dependent rates. Our model accounts not only for the number of cold and warm hours but also for their order. We used sets of empirical flowering and temperature data to fit the model parameters, applying numerical constrained optimization techniques, and successfully validated the model outcomes. Our model more accurately predicts flowering under winters with warm periods yielding low-to-moderate flowering and is more robust compared to previous models. This model is the first step toward a practical predictive tool, applicable under various temperature conditions.

Flux balance analysis has proven to be a successful approach in metabolic engineering and systems biology, for predicting intracellular fluxes of large genome-scale networks and the essentiality of genes encoding enzymes and regulatory factors. Flux balance analysis (FBA) relies on a key assumption of a metabolic state being persistent ("steady") over a given time frame. This assumption works well for microbial growth because of the ease with which microbial media can be fixed, biomass can be decomposed, and growth rates can be measured. However, the assumption is far less tenable for the cells and tissues of complex multicellular organisms, particularly if any integrated data is sampled from a heterogeneous collection of developing cells continually interacting between and across tissues. These will likely exhibit transient metabolic states equilibrating over varying timescales, and many FBA studies in complex organisms typically either ignore time as a parameter, or integrate data taken over long timescales (days/weeks). In this work, we adopt and modify a previously published machine learning approach that hybridized several aspects of a constraint-based approach with machine-learning in order to predict growth. This study introduces a Machine Learning-FBA framework for plant tissues that accommodates transient state dynamics, at the cost of violating the steady-state assumption, in order to enable more accurate flux estimation in plant tissues. For our case study we reconstruct the metabolism of the plastid of Poplar and Sorghum, integrating data sampled from leaf tissue under varying levels of iron bioavailability. We show that the approach gives us more realistic insights into plastidial metabolism, indicates where our metabolic reconstruction could be improved, and still allows us to draw novel hypotheses on the impact of metal bioavailability on plant leaves.

Grasses grow a series of phytomers during development. The distance between successive leaves is determined by internode lengths. Grasses exhibit genetic, developmental, and environmental variability in phytomer number, but how this affects internode length, biomass, and height is unknown. We hypothesized that a generalized mathematical model of phytomer development wherein between-phytomer competition influences internode length distributions would be sufficient to explain internode length patterns in two Setaria genotypes: weedy A10 and domesticated B100. Our model takes a novel approach that includes the vegetative growth of leaf blade, sheath, and internode at the individual phytomer level, and the shift to reproductive growth. To validate and test our mathematical model, we carried out a greenhouse experiment. We found that the rate of leaf emergence is consistent for both genotypes across development, and that the length of time spent elongating for the leaf and internode can be described as the ratio between the time of phytomer emergence and the elongation completion time. The validated model was simulated across all possible parameter values to predict the influence of phytomer number on internode length. This analysis predicts that different internode length distributions across different numbers of total phytomers are an emergent property, rather than a genotype-specific property requiring genotype-specific models. We applied the model to internode length only field data of S. italica accession B100, grown under both well-watered and drought conditions. The model predicts that droughted plants reduce leaf elongation time, reduce resource allocation to the internodes, and overall experience slower growth. Together, model and data suggest that allometric patterns are driven by competition for resources among phytomer and the shift to reproductive growth in Setaria. The resulting model enables us to predict growth dynamics and final allometries at the phytomer level.

Current global models of vegetation dynamics are largely carbon (C) source-driven, with behaviour primarily determined by the environmental responses of photosynthesis. However, real plants operate as integrated wholes, with feedbacks between sources, such as photosynthesis, and sinks, such as growth, resulting in homeostatic concentrations of metabolites such as sugars. A parsimonious approach to implementing this homeostatic coupling of C sources and sinks in a tree growth model is presented, and its implications for the responses of net photosynthesis and growth to environmental factors and tree size assessed. Hill functions describe inhibition of C sources (net photosynthesis) and activation of sinks (structural growth) as sucrose concentration increases. The model is parameterised for a typical tree growing at a site in the Amazonian rainforest and its qualitative behaviour is found to be consistent with observations. A key outcome is that sinks and sources strongly regulate each other. Hence environmental factors that affect potential net photosynthesis, such as atmospheric CO2, have greatly reduced effects on growth when homeostatic feedbacks from sucrose concentrations are considered. For example, compared with a C-source-only-driven approach (as in most current global models), the response of tree biomass for a tree currently 300 yr old, to increasing atmospheric CO2 projected to the end of this century under a high scenario, is reduced by ca.77%, from +122% to +29%, with net photosynthesis and growth rate responses reduced by a similar amount. Furthermore, in this coupled approach, any direct controls on growth (either environmental or through phenological controls on xylogensis) will influence source activity through the sucrose feedback. For example, a reduction in potential growth through temperature constraints on cell-wall construction increases sucrose concentrations, resulting in a compensating reduction in net photosynthesis. While net photosynthesis controls growth, growth controls net photosynthesis. In addition, we find a strong effect of changing tree allometry on C source-sink relations as the tree grows. Larger trees are less source-limited due to a higher ratio of sapwood area (and hence potential C assimilation rate) to potential growth rate, consistent with the observed decline in growth response to atmospheric CO2 as trees age. We suggest that the implications of including C source-sink coupling in models of vegetation dynamics, such as dynamic global vegetation models, are likely to be profound.